On the long-established classification problems in general relativity we take a novel perspective by adopting fruitful techniques from machine learning and modern data-science. In particular, we model Petrov's classification of spacetimes, and show that a feed-forward neural network can achieve high degree of success. We also show how data visualization techniques with dimensionality reduction can help analyze the underlying patterns in the structure of the different types of spacetimes.

翻译：关于长期存在的一般相对论分类问题,我们从新角度从机器学习和现代数据科学中采用富有成效的技术。 特别是,我们模拟了Petrov对空间时间的分类,并表明进化前神经网络可以取得高度成功。 我们还展示了数据可视化技术如何帮助分析不同类型空间时间结构的基本模式。